Curriculum

Within 7 weeks, we will be covering machine learning and all topics pertaining to its requirements.

 

Many topics in programming, data analysis and modeling will be covered throughout the course

 

After this course, you will gain the knowledge required to analyze data, design models and successfully deploy a machine learning project while preparing presentations and following roadmaps.

  • Week 1 Find and Pre-Process Big Data
  • Week 2 Exploratory Data Analysis
  • Week 3 Machine Learning Techniques
  • Week 4 Machine Learning Coding
  • Week 5 Team Project 1
  • Week 6 Team Project 2
  • Week 7 Creating a Simple Data Dashboard

Topics Covered

Working with Big Data

Learning how to fetch and process big data as well as conduct exploratory data analysis.

  • Assessing your Data
  • Significance & Correlations
  • Data Visualization Tools
  • Different Visualization Approaches

Machine Learning Coding & Techniques

Learn the languages and techniques involved in machine learning.

  • Selecting Complexity Levels
  • Hyperparameters in Models
  • Dev & Production Environments
  • Auto-ML Solutions

Project Design

Build your machine learning model along with everything else needed to succeed.

  • Designing Project Roadmaps
  • Creating a Use-case
  • Technical Presentations
  • Constructing Data Dashboards

Instructors

Arman Mottaghi

Arman is the CEO of Lambda Science – a data science firm in the construction industry – and also serve as a Board Director in the BC Sustainable Energy Association. The product he currently manages; StepWin, uses machine learning to make residential buildings’ energy simulation.

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Johannes Harmse

Johannes first discovered the potential of machine learning while working in the healthcare industry. He currently works as a data science consultant solving problems across various domains. Outside of work, he serves as an AI event organizer and content creator.

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Xinbin Huang

Xinbin currently works as a Data Analyst at Electronic Arts Vancouver – a leading company in the gaming industry. Leveraging the big data infrastructure, he builds data pipeline and transfer data insights into business values. Before joining EA, he was a data scientist that built and deployed machine learning model for production.

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Matt Toledo

Matt has been working with different facets of Machine Learning for several years, from basic supervised ML to Deep Reinforcement Learning. Currently, Matt works as a mentor for Udacity and as an instructor and content creator for a local AI meet up in Vancouver.

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Ian Flores

Ian is a Data Scientist with experience working with government agencies, NGOs and academia. He has applied Machine Learning in research contexts to solve a range of conservation problems in the Caribbean and South America. Currently he focuses on developing new data science education material for non-academic contexts.

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